SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection

• URL: http://arxiv.org/abs/2403.19096v1
• Date: Thu, 28 Mar 2024 02:20:03 GMT
• Title: SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection
• Authors: Xin-Cheng Wen, Cuiyun Gao, Shuzheng Gao, Yang Xiao, Michael R. Lyu,
• Abstract summary: We propose a Structured Natural Language Comment tree-based vulnerAbiLity dEtection framework based on the pre-trained models. The proposed Structured Natural Language Comment Tree (SCT) integrates the semantics of code statements with code execution sequences.
• Score: 36.37244302912536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However, the existing pre-trained model-based approaches generally employ code sequences as input during prediction, and may ignore vulnerability-related structural information, as reflected in the following two aspects. First, they tend to fail to infer the semantics of the code statements with complex logic such as those containing multiple operators and pointers. Second, they are hard to comprehend various code execution sequences, which is essential for precise vulnerability detection. To mitigate the challenges, we propose a Structured Natural Language Comment tree-based vulnerAbiLity dEtection framework based on the pre-trained models, named SCALE. The proposed Structured Natural Language Comment Tree (SCT) integrates the semantics of code statements with code execution sequences based on the Abstract Syntax Trees (ASTs). Specifically, SCALE comprises three main modules: (1) Comment Tree Construction, which aims at enhancing the model's ability to infer the semantics of code statements by first incorporating Large Language Models (LLMs) for comment generation and then adding the comment node to ASTs. (2) Structured Natural Language Comment Tree Construction}, which aims at explicitly involving code execution sequence by combining the code syntax templates with the comment tree. (3) SCT-Enhanced Representation, which finally incorporates the constructed SCTs for well capturing vulnerability patterns.

Related papers

• Line-level Semantic Structure Learning for Code Vulnerability Detection [44.29771620061153]
  We introduce the Code Structure-Aware Network through Line-level Semantic Learning. It comprises four components: code preprocessing, global semantic awareness, line semantic awareness, and line semantic structure awareness. The CSLS model outperforms the state-of-the-art baselines in code vulnerability detection, achieving 70.57% accuracy on the Devign dataset and a 49.59% F1 score on the Reveal dataset.
  arXiv  Detail & Related papers  (2024-07-26T17:15:58Z)
• Physics of Language Models: Part 1, Learning Hierarchical Language Structures [51.68385617116854]
  Transformer-based language models are effective but complex, and understanding their inner workings is a significant challenge. We introduce a family of synthetic CFGs that produce hierarchical rules, capable of generating lengthy sentences. We demonstrate that generative models like GPT can accurately learn this CFG language and generate sentences based on it.
  arXiv  Detail & Related papers  (2023-05-23T04:28:16Z)
• Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation [61.50286000143233]
  ChainCoder is a program synthesis language model that generates Python code progressively. A tailored transformer architecture is leveraged to jointly encode the natural language descriptions and syntactically aligned I/O data samples.
  arXiv  Detail & Related papers  (2023-04-28T01:47:09Z)
• Benchmarking Language Models for Code Syntax Understanding [79.11525961219591]
  Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding. In this work, we perform the first thorough benchmarking of the state-of-the-art pre-trained models for identifying the syntactic structures of programs. Our findings point out key limitations of existing pre-training methods for programming languages, and suggest the importance of modeling code syntactic structures.
  arXiv  Detail & Related papers  (2022-10-26T04:47:18Z)
• What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code [32.345301158791045]
  Pre-trained language models for source code have been proposed to model the context of code. These models leverage masked pre-training and Transformer. It is not clear why these models work and what feature correlations they can capture.
  arXiv  Detail & Related papers  (2022-02-14T16:22:10Z)
• CLSEBERT: Contrastive Learning for Syntax Enhanced Code Pre-Trained Model [23.947178895479464]
  We propose CLSEBERT, a Constrastive Learning Framework for Syntax Enhanced Code Pre-Trained Model. In the pre-training stage, we consider the code syntax and hierarchy contained in the Abstract Syntax Tree (AST) We also introduce two novel pre-training objectives. One is to predict the edges between nodes in the abstract syntax tree, and the other is to predict the types of code tokens.
  arXiv  Detail & Related papers  (2021-08-10T10:08:21Z)
• Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting [15.28941592388958]
  Abstract Syntax Tree (AST), which depicts the source code's syntactic structure, has been incorporated to guide the generation of code summaries. Existing AST based methods suffer from the difficulty of training and generate inadequate code summaries. We present the Block-wise Abstract Syntax Tree Splitting method (BASTS), which fully utilizes the rich tree-form syntax structure in ASTs.
  arXiv  Detail & Related papers  (2021-03-14T05:04:06Z)
• GraphCodeBERT: Pre-training Code Representations with Data Flow [97.00641522327699]
  We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. We use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement.
  arXiv  Detail & Related papers  (2020-09-17T15:25:56Z)
• Exploring Software Naturalness through Neural Language Models [56.1315223210742]
  The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
  arXiv  Detail & Related papers  (2020-06-22T21:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.